Evidence map›Paper›PMID 32849794›Full record

ArticleFrontiers in genetics2020

Systematic Evaluation of the Diagnostic and Prognostic Significance of Competitive Endogenous RNA Networks in Prostate Cancer.

Zihu Guo, Liang Han, Yingxue Fu, Ziyin Wu, Yaohua Ma, Yueping Li, Haiqing Wang, Li Jiang, Shengnan Liang, Zhenzhong Wang and 4 more

Abstract read
In one paragraph

Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Zihu GuoCollege of Life Science, Northwest A&F University, Yangling, China.
Liang HanDepartment of Andrology, Fangshan Hospital, Beijing University of Chinese Medicine, Beijing, China.
Yingxue FuCollege of Life Science, Northwest A&F University, Yangling, China.
Ziyin WuState Key Laboratory of New-Tech for Chinese Medicine Pharmaceutical Process, Lianyungang, China.
Yaohua MaCollege of Life Science, Northwest University, Xi'an, China.
Yueping LiKey Laboratory of Xinjiang Phytomedicine Resource and Utilization, Ministry of Education, Shihezi University, Shihezi, China.
Haiqing WangCollege of Life Science, Northwest University, Xi'an, China.
Li JiangKey Laboratory of Xinjiang Phytomedicine Resource and Utilization, Ministry of Education, Shihezi University, Shihezi, China.
Shengnan LiangSchool of Chemistry and Pharmacy, Northwest A&F University, Yangling, China.
Zhenzhong WangState Key Laboratory of New-Tech for Chinese Medicine Pharmaceutical Process, Lianyungang, China.
Furong LiTranslational Medicine Collaborative Innovation Center, The Second Clinical Medical College (Shenzhen People's Hospital), Integrated Chinese and Western Medicine Postdoctoral Research Station, Jinan University, Shenzhen, China.
Wei XiaoState Key Laboratory of New-Tech for Chinese Medicine Pharmaceutical Process, Lianyungang, China.
Jingbo WangTranslational Medicine Collaborative Innovation Center, The Second Clinical Medical College (Shenzhen People's Hospital), Integrated Chinese and Western Medicine Postdoctoral Research Station, Jinan University, Shenzhen, China.
Yonghua WangCollege of Life Science, Northwest A&F University, Yangling, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Long non-coding RNA (lncRNA)-mediated competitive endogenous RNA (ceRNA) networks act as essential mechanisms in tumor initiation and progression, but their diagnostic and prognostic significance in prostate cancer (PCa) remains poorly understood. Presently, using the RNA expression data derived from multiple independent PCa-related studies, we constructed a high confidence and PCa-specific core ceRNA network by employing three lncRNA-gene inference approaches and key node filter strategies and then established a logistic model and risk score formula to evaluate its diagnostic and prognostic values, respectively. The core ceRNA network consists of 10 nodes, all of which are significantly associated with clinical outcomes. Combination of expression of the 10 ceRNAs with a logistic model achieved AUC of ROC and PR curve up to ∼96 and 99% in excluding normal prostate samples, respectively. Additionally, a risk score formula constructed with the ceRNAs exhibited significant association with disease-free survival. More importantly, utilizing the expression of RNAs in the core ceRNA network as a molecular signature, the TCGA-PRAD cohort was divided into four novel clinically relevant subgroups with distinct expression patterns, highlighting a feasible way for improving patient stratification in the future. Overall, we constructed a PCa-specific core ceRNA network, which provides diagnostic and prognostic value.

Indexed as

bioinformatics analysisceRNA networkdiagnostic modelprognosisprostate cancer

Identifiers

PMID32849794
PMCPMC7406720

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.